In our first experiment, the three proposed network architectures are evaluated in a centralized training scenario. First, we train the models using the original URBAN-SED dataset [1]} to validate that the architectures work as expected on a well-known dataset. Then, we train each of the three models using the FL training sets. We evaluate the models, first, with batch normalization, and then, replace all batch normalization layers with group normalization to ensure that this substitution for mitigating non-IID issues in FL [2]} does not significantly affect the baseline results.
All models are trained for 50 epochs with early stopping using a patience of 25 epochs, which monitors the validation loss. We use the Adam optimizer [3]} with a cosine learning rate schedule in the range \([1e-04, 1e-06]\) .
